The CAT model exhibits broadly consistent adequacy across evolutionary timescales

Clicks: 7
ID: 337755
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #128 of 129 articles by views in systematic biology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 129 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract Amino-acid substitution models are central to phylogenomic inference, yet their adequacy (i.e. whether they realistically describe features of real datasets) remains insufficiently tested. Here, we evaluated the adequacy of seven widely used models of amino acid substitution across empirical datasets spanning diverse evolutionary timescales. We focused specifically on the model's ability to capture across-site compositional heterogeneity in empirical data, by evaluating complementary metrics that quantify both site-level diversity and amino-acid frequency spectrum. Of the models we tested, LG+G, representing the site-homogeneous models, always fails to adequately describe the real data. Among site-heterogeneous mixture models, Poisson+CAT+G consistently achieves a close match to empirical data, while other finite-mixture models show intermediate or mixed behavior, with performance varying by datasets and by metric. Taken together, our results highlight that adequacy tests are important for identifying model strengths and limitations in accounting for across-site compositional heterogeneity.
Reference Key
openalex_W7143441539 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal systematic biology
Year 2026
DOI
10.1093/sysbio/syag082
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.